Proposition REF  provides a Chernoff-type lower bound that can be leveraged in the analysis of node attribute nonlinear propagation. From Lemma REF , the behavior of nonlinearized attribute can be interpreted as either Gaussian or Rademacher random variables in the limited attributed information setting. Follow the derivation of Theorem 9 in [1]} and leverage the (reversed) Chernoff-Cramer inequality for upper bound, we have the following results:
